25 citations · 75 across the 32 of their papers we have counts for
5 papers · 1 filter
Reducing Variance in Meta-Learning via Laplace Approximation for Regression Tasks
Alfredo Reichlin, Gustaf Tegnér, Miguel Vasco +3
Given a finite set of sample points, meta-learning algorithms aim to learn an optimal adaptation strategy for new, unseen tasks. Often, this data can be ambiguous as it might belon…
Dance Style Transfer with Cross-modal Transformer
Wenjie Yin, Hang Yin, Kim Baraka +2
We present CycleDance, a dance style transfer system to transform an existing motion clip in one dance style to a motion clip in another dance style while attempting to preserve mo…
On the Subspace Structure of Gradient-Based Meta-Learning
Gustaf Tegnér, Alfredo Reichlin, Hang Yin +2
In this work we provide an analysis of the distribution of the post-adaptation parameters of Gradient-Based Meta-Learning (GBML) methods. Previous work has noticed how, for the cas…
Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series
Shuangshuang Chen, Sihao Ding, Yiannis Karayiannidis +1
Learning generative models and inferring latent trajectories have shown to be challenging for time series due to the intractable marginal likelihoods of flexible generative models.…
Imitating by generating: deep generative models for imitation of interactive tasks
Judith Bütepage, Ali Ghadirzadeh, Özge Öztimur Karadag +2
To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals. Humans acquire these skills in infancy and early childhood mostly by imitati…